Remove base counts from the dataset

src/model.py

@@ -86,7 +86,6 @@ def run(data_file, label_file, seed_value=42) -> None:
         epochs=EPOCHS,
         validation_data=eval_data,
         callbacks=[tensorboard],
-        verbose=0,
     )
     print("Training complete. Obtaining final metrics...")
     show_metrics(model, eval_data, test_data)

src/preprocessing.py

@@ -1,31 +1,21 @@
-from typing import List, Tuple
+from typing import Dict, List, Tuple
 
-from Bio.motifs import create
 from Bio.SeqIO import parse
 from numpy.random import random
-from tensorflow import Tensor, int64, stack, cast, int32
-from tensorflow.sparse import to_dense
+from tensorflow import Tensor, int64
 from tensorflow.data import TFRecordDataset
-from tensorflow.io import (
-    FixedLenFeature,
-    TFRecordWriter,
-    VarLenFeature,
-    parse_single_example,
-)
+from tensorflow.io import TFRecordWriter, VarLenFeature, parse_single_example
+from tensorflow.sparse import to_dense
 from tensorflow.train import Example, Feature, Features, Int64List
 
 from constants import *
 
 
-def generate_example(sequence, label, base_counts) -> bytes:
+def generate_example(sequence, label) -> bytes:
     """
     Create a binary-string for each sequence containing the sequence and the bases' counts
     """
     schema = {
-        "A_counts": Feature(int64_list=Int64List(value=[sum(base_counts["A"])])),
-        "C_counts": Feature(int64_list=Int64List(value=[sum(base_counts["C"])])),
-        "G_counts": Feature(int64_list=Int64List(value=[sum(base_counts["G"])])),
-        "T_counts": Feature(int64_list=Int64List(value=[sum(base_counts["T"])])),
         "sequence": Feature(int64_list=Int64List(value=encode_sequence(sequence))),
         "label": Feature(int64_list=Int64List(value=encode_sequence(label))),
     }
@@ -48,11 +38,9 @@ def read_fastq(data_file, label_file) -> List[bytes]:
     examples = []
     with open(data_file) as data, open(label_file) as labels:
         for element, label in zip(parse(data, "fastq"), parse(labels, "fastq")):
-            motifs = create([element.seq])
             example = generate_example(
                 sequence=str(element.seq),
                 label=str(label.seq),
-                base_counts=motifs.counts,
             )
             examples.append(example)
     return examples
@@ -77,19 +65,14 @@ def create_dataset(
                 test.write(element)
 
 
-def transform_features(parsed_features) -> List[Tensor]:
+def transform_features(parsed_features) -> Dict[str, Tensor]:
     """
-    Cast and transform the parsed features of an Example into a list of Tensors
+    Transform the parsed features of an Example into a list of dense Tensors
     """
+    features = {}
     sparse_features = ["sequence", "label"]
-    for feature in sparse_features:
-        parsed_features[feature] = cast(parsed_features[feature], int32)
-        parsed_features[feature] = to_dense(parsed_features[feature])
-    for base in BASES:
-        parsed_features[f"{base}_counts"] = cast(
-            parsed_features[f"{base}_counts"], int32
-        )
-    features = list(parsed_features.values())[:-1]
+    for element in sparse_features:
+        features[element] = to_dense(parsed_features[element])
     return features
 
 
@@ -98,16 +81,12 @@ def process_input(byte_string) -> Tuple[Tensor, Tensor]:
     Parse a byte-string into an Example object
     """
     schema = {
-        "A_counts": FixedLenFeature(shape=[1], dtype=int64),
-        "C_counts": FixedLenFeature(shape=[1], dtype=int64),
-        "G_counts": FixedLenFeature(shape=[1], dtype=int64),
-        "T_counts": FixedLenFeature(shape=[1], dtype=int64),
         "sequence": VarLenFeature(dtype=int64),
         "label": VarLenFeature(dtype=int64),
     }
     parsed_features = parse_single_example(byte_string, features=schema)
     features = transform_features(parsed_features)
-    return stack(features, axis=-1), parsed_features["label"]
+    return features["sequence"], features["label"]
 
 
 def read_dataset(filepath) -> TFRecordDataset: